Mining Temporal Social Network Patterns
| Autor: | Hsun-Ping Hsieh, 解巽評 |
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| Rok vydání: | 2009 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 97 With an increasing interest in social network applications, how to find meaningful patterns from social networks has attracted more and more attention. The interactions in a social network can naturally be modeled by a temporal network, where a node in the network represents an individual, and an edge between two nodes denotes the interaction between two individuals in a certain time interval. Mining frequent patterns in temporal social networks can help us discover frequent interaction behaviors. Therefore, in this thesis, we propose a novel algorithm, TSP-Miner (Temporal Social network Patterns Miner), to mine frequent closed temporal social network patterns. The proposed algorithm consists of two phases. First, we find all frequent patterns of length one in the database. Second, for each pattern found in the first phase, we recursively generate frequent patterns by a frequent pattern tree in a depth-first search manner. During the mining process, we eliminate impossible candidates and check whether the frequent patterns are closed or not. Since the TSP-Miner only needs to scan the database once and doesn’t generate unnecessary candidates, it is more efficient and scalable than the modified Apriori algorithm. The experiment results show that the TSP-Miner outperforms the modified Apriori in both synthetic and real datasets. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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